scholarly journals TIME SERIES ANALYSIS OF REMOTE SENSING OBSERVATIONS FOR CITRUS CROP GROWTH STAGE AND EVAPOTRANSPIRATION ESTIMATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 1037-1042 ◽  
Author(s):  
S. A. Sawant ◽  
M. Chakraborty ◽  
S. Suradhaniwar ◽  
J. Adinarayana ◽  
S. S. Durbha
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Growth Stage ◽  
Crop Growth ◽  
Water Requirement ◽  
Growth Stages ◽  
Crop Water ◽  
Horticultural Crops ◽  
Crop Growth Stages ◽  
Crop Growth Stage

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (<a href="http://earthexplorer.usgs.gov/"target="_blank">http://earthexplorer.usgs.gov/</a>). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.

scholarly journals TIME SERIES ANALYSIS OF REMOTE SENSING OBSERVATIONS FOR CITRUS CROP GROWTH STAGE AND EVAPOTRANSPIRATION ESTIMATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 1037-1042 ◽  
Author(s):  
S. A. Sawant ◽  
M. Chakraborty ◽  
S. Suradhaniwar ◽  
J. Adinarayana ◽  
S. S. Durbha
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Growth Stage ◽  
Crop Growth ◽  
Water Requirement ◽  
Growth Stages ◽  
Crop Water ◽  
Horticultural Crops ◽  
Crop Growth Stages ◽  
Crop Growth Stage

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (<a href="http://earthexplorer.usgs.gov/"target="_blank">http://earthexplorer.usgs.gov/</a>). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.

scholarly journals Assessment of crop water requirement of field pea (Pisum sativum L.) in foothills valley areas of Manipur, North East India

2021 ◽  
Vol 23 (3) ◽  
pp. 306-309
Author(s):  
LAISHRAM KANTA SINGH ◽  
INGUDAM BHUPENCHANDRA ◽  
S. ROMA DEVI
Keyword(s):  
Pisum Sativum ◽  
Water Demand ◽  
Crop Growth ◽  
Water Requirement ◽  
Crop Water Requirement ◽  
Field Pea ◽  
Growth Stages ◽  
Crop Water ◽  
Pisum Sativum L ◽  
Crop Growth Stages

The purpose of this study was to assess the evapotranspiration in field pea (Pisum sativum L.) in foothills valley areas of Manipur using the Hargreaves-Samani equation to predict the plant water demand. The crop coefficient (Kc) values ranged between 0.45 and 1.28 during the crop growth stages of field pea for the five crop seasons (2013-18). The average five-year effective rainfall was estimated to be 59.0 mm, with standard deviation (SD±) ranging between 4.4 to 35.1 mm. The average crop water requirement for field pea was estimated to be 221.0 mm and the average water demand for different crop growth stages of field pea was estimated to be 20.0 mm (initial stage), 52.0 mm (development stage), 100.0 mm (mid-season) and 49.0 mm (late season). Thus, the information generated may help in effective management of crop water requirements for sustainable crop production including field pea in the region.

scholarly journals Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Feng Gao ◽  
Xiaoyang Zhang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Real Time ◽  
Land Surface ◽  
Vegetation Index ◽  
Growing Season ◽  
Crop Growth ◽  
Growth Stages ◽  
Crop Phenology ◽  
Crop Growth Stages

Crop phenology is critical for agricultural management, crop yield estimation, and agroecosystem assessment. Traditionally, crop growth stages are observed from the ground, which is time-consuming and lacks spatial variability. Remote sensing Vegetation Index (VI) time series has been used to map land surface phenology (LSP) and relate to crop growth stages mostly after the growing season. In recent years, high temporal and spatial resolution remote sensing data have allowed near-real-time mapping of crop phenology within the growing season. This paper summarizes two classes of near-real-time mapping methods, i.e., curve-based and trend-based approaches. The curve-based approaches combine the time series VIs and crop growth stages from historical years with the current observations to estimate crop growth stages. The curve-based approaches are capable of a short-term prediction. The trend-based approaches detect upward or downward trends from time series and confirm the trends using the increasing or decreasing momentum and VI thresholds. The trend-based approaches only use current observations. Both curve-based and trend-based approaches are promising in mapping crop growth stages timely. Nevertheless, mapping crop phenology near real-time is challenging since remote sensing observations are not always sensitive to crop growth stages. The accuracy of crop phenology detection depends on the frequency and availability of cloud-free observations within the growing season. Recent satellite datasets such as the harmonized Landsat and Sentinel-2 (HLS) are promising for mapping crop phenology within the season over large areas. Operational applications in the near future are feasible.

scholarly journals Is what you see what you get? The relationship between field observed and actual aphid parasitism rates in canola crops

2021 ◽  
Author(s):  
Samantha Ward ◽  
Paul A. Umina ◽  
Hazel Parry ◽  
Amber Balfour-Cunningham ◽  
Xuan Cheng ◽  
...  
Keyword(s):  
Growth Stage ◽  
Green Peach Aphid ◽  
South Australia ◽  
Field Work ◽  
Crop Growth ◽  
Diaeretiella Rapae ◽  
Growth Stages ◽  
Parasitoid Species ◽  
Parasitism Rates ◽  
Crop Growth Stage

AbstractBACKGROUNDEstimating parasitoid abundance in the field can be difficult, even more so when attempting to quantify parasitism rates and the ecosystem service of biological control that parasitoids can provide. To understand how ‘observed’ parasitism rates (in-field mummy counts) of the green peach aphid, Myzus persicae (Sulzer) (Homoptera: Aphididae) translate to ‘actual’ parasitism rates (laboratory-reared parasitoid counts), field work was undertaken in Australian canola fields over a growing season. Parasitoids were reared within a controlled laboratory setting.RESULTSTotal observed and actual parasitism rates of M. persicae varied considerably across regions, but less so on a field level. Overall, actual parasitism was on average 2.4 times higher than that observed in the field, with rates an average of 4-fold higher in South Australia. As crop growth stage progressed, the percentage of mummies observed increased. Percentage of parasitoids reared also increased with crop growth stage, averaging 3.4% during flowering and reaching 14.4% during podding/senescing. Although there was a greater diversity of reared parasitoid species at later crop growth stages, actual parasitism rate was unaffected by parasitoid species. Diaeretiella rapae was the most commonly reared parasitoid, increasing in abundance with crop growth stage.CONCLUSIONThese findings indicate that mummy counts alone do not provide a clear representation of parasitism within fields.

Irrigation of field peas (Pisum sativum): response to timing at different crop growth stages

1999 ◽  
Vol 132 (4) ◽  
pp. 417-424 ◽  
Author(s):  
C. M. KNOTT
Keyword(s):  
Pisum Sativum ◽  
Vegetative Growth ◽  
Growth Stage ◽  
Crop Growth ◽  
Yield Response ◽  
Growth Stages ◽  
Field Peas ◽  
Crop Growth Stages ◽  
Different Growth Stages

The response of two cultivars of dry harvest field peas (Pisum sativum), Solara and Bohatyr, to irrigation at different growth stages was studied on light soils overlying sand in Nottinghamshire, England in 1990, when the spring was particularly dry, in 1991 which had a dry spring and summer and in contrast, 1992, when rainfall was greater compared with the long-term (40 year) mean.Solara, short haulmed and semi-leafless was more sensitive to drought than the tall conventional-leaved cultivar Bohatyr and gave a greater yield response to irrigation, particularly at the vegetative growth stage in the first two dry years 1990 and 1991, of 108% and 55% respectively, compared with unirrigated plots. Bohatyr was less sensitive to the timing of single applications.In all years, peas irrigated throughout on several occasions produced the highest yields, but this was the least efficient use of water.

scholarly journals Rhizopus Head Rot of Confectionery Sunflower: Effects on Yield Quantity and Quality and Implications for Disease Management

1997 ◽  
Vol 87 (12) ◽  
pp. 1226-1232 ◽  
Author(s):  
D. Shtienberg
Keyword(s):  
Growth Stage ◽  
Field Experiments ◽  
Rhizopus Oryzae ◽  
Development Stage ◽  
Crop Growth ◽  
Growth Stages ◽  
Yield Quality ◽  
Crop Growth Stage ◽  
Head Rot ◽  
The Individual

The effects of Rhizopus head rot, caused by Rhizopus oryzae, on the yield of confectionery sunflower and its quality were studied in field experiments conducted from 1994 to 1996. The extent of yield loss was related to the crop growth stage at inoculation. When heads were inoculated at the budding stage, loss was not apparent, because inoculated heads were not infected. When inoculated at the anthesis stage, loss was relatively high (42.5 to 99.1%), and both the number of achenes per head and the individual achene weight were reduced. When heads were inoculated at the seed development stage, yield was not reduced significantly (although the entire receptacle was rotted). Effects of Rhizopus head rot on measures of yield quality were examined as well. Inoculation with R. oryzae did not affect the size of the achenes at any crop growth stage. In contrast, the incidence of discolored achenes (an external sign of nutmeats with a bitter off-flavor) was affected by the disease at all crop growth stages. A survey in eight commercial fields from 1992 to 1996 found that, by the end of the season, incidence of disease ranged from 2.3 to 17.4%. However, since disease intensified late, resultant yield losses were minor and did not exceed 3.1%. Loss figures were estimated by means of a model that was developed and validated in the field experiments. The disease did affect the incidence of discolored achenes. Thus, the conclusion drawn is that the effects of Rhizopus head rot in confectionery sunflower on crop yield is of minimal concern, at least when disease intensifies late, as was the case in the studied fields, but management of the disease should be considered in some situations. The objectives would be to prevent a reduction in yield quality, not yield quantity.

Diquat residues in lentil following preharvest treatment

1991 ◽  
Vol 71 (2) ◽  
pp. 413-418
Author(s):  
Allan Cessna
Keyword(s):  
Key Words ◽  
Spectrophotometric Determination ◽  
Analytical Method ◽  
Lens Culinaris ◽  
Growth Stage ◽  
Crop Growth ◽  
Growth Stages ◽  
Limit Of Quantification ◽  
Crop Growth Stages

In a 2-yr study, residues of diquat were spectrophotometrically determined in lentil (Lens culinaris Medik.) seed and straw/chaff following preharvest treatment using 0.56 kg ha−1 at three crop growth stages. Diquat residues ranged from 12.9 to 17.3 mg kg−1 in the lentil straw/chaff one day after application and decreased to 1.1 to 6.0 mg kg−1 2 wk later. Diquat residues in the seed were in the order of 0.05 mg kg−1 or less regardless of time of sampling after spraying or growth stage of the crop at application. The limit of quantification of the analytical method was 0.04 mg kg−1, and recoveries of diquat from fortified seed and straw were in the order of 70%. Key words: Diquat, lentil, residues, spectrophotometric determination

scholarly journals INTEGRATION OF HUMAN PARTICIPATORY SENSING AND ARCHIVES OF REMOTE SENSING OBSERVATIONS FOR FIELD LEVEL CROP PHENOLOGY ESTIMATION

2018 ◽  
Vol XLII-4 ◽  
pp. 547-550
Author(s):  
S. A. Sawant ◽  
J. D. Mohite ◽  
S. Pappula
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Specific Information ◽  
Crop Phenology ◽  
Field Level ◽  
Sentinel 2

<p><strong>Abstract.</strong> The rise in global population has increased food and water demand thereby causing excessive pressure on existing resources. In developing countries with fragmented land holdings there exists constant pressure on available water and land resources. Obtaining field scale crop specific information is challenging task. Advent of open freely available multi-temporal remote sensing observations with improved radiometric resolution the possibilities for near real / real time applications has increased. In this study and an attempt has been made to establish operational model for field level crop growth monitoring using integrated approach of crowd sourcing and time series of remote sensing observations. The time series of Sentinel 2 (A and B) satellite has been used to estimate crop growth related components such as vegetation indices and crop growth stage and crop phenology. In initial stage high valued cereal crop Wheat has been selected. The field level information (i.e. 108 Wheat fields) collected using mobile based agro-advisory platform mKRISHI&amp;reg; has been used to extract time series of Sentinel 2 observations (44 scenes for year 2016 and 2018). The moving average has been used for filling gaps in the time series of vegetation indices. The BFAST and GreenBrown package in R were used for detecting breaks in vegetation index time series and estimating crop growth stages. Analysis shows that the estimated crop phenology parameters were in better agreement with the field observations. In future more crops from different agro-climatic conditions will be considered for providing field level crop management advisory.</p>

scholarly journals National Crop Mapping Using Sentinel-1 Time Series: A Knowledge-Based Descriptive Algorithm

2021 ◽  
Vol 13 (5) ◽  
pp. 846
Author(s):  
Carole Planque ◽  
Richard Lucas ◽  
Suvarna Punalekar ◽  
Sebastien Chognard ◽  
Clive Hurford ◽  
...  
Keyword(s):  
Time Series ◽  
Crop Growth ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Growth Stages ◽  
Knowledge Based ◽  
Crop Type ◽  
Crop Mapping ◽  
Crop Growth Stages ◽  
Crop Types

National-level mapping of crop types is important to monitor food security, understand environmental conditions, inform optimal use of the landscape, and contribute to agricultural policy. Countries or economic regions currently and increasingly use satellite sensor data for classifying crops over large areas. However, most methods have been based on machine learning algorithms, with these often requiring large training datasets that are not always available and may be costly to produce or collect. Focusing on Wales (United Kingdom), the research demonstrates how the knowledge that the agricultural community has gathered together over past decades can be used to develop algorithms for mapping different crop types. Specifically, we aimed to develop an alternative method for consistent and accurate crop type mapping where cloud cover is quite persistent and without the need for extensive in situ/ground datasets. The classification approach is parcel-based and informed by concomitant analysis of knowledge-based crop growth stages and Sentinel-1 C-band SAR time series. For 2018, crop type classifications were generated nationally for Wales, with regional overall accuracies ranging between 85.8% and 90.6%. The method was particularly successful in distinguishing barley from wheat, which is a major source of error in other crop products available for Wales. This study demonstrates that crops can be accurately identified and mapped across a large area (i.e., Wales) using Sentinel-1 C-band data and by capitalizing on knowledge of crop growth stages. The developed algorithm is flexible and, compared to the other methods that allow crop mapping in Wales, the approach provided more consistent discrimination and lower variability in accuracies between classes and regions.

scholarly journals Estimation of crop water requirement for rice- wheat and rice- maize cropping system using CROPWAT model for Pusa, Samastipur district, Bihar

2021 ◽  
Vol 8 (2) ◽  
pp. 143-148
Author(s):  
RAVISH CHANDRA ◽  
SHABANAM KUMARI
Keyword(s):  
Meteorological Data ◽  
Cropping System ◽  
Crop Coefficient ◽  
Water Requirement ◽  
Crop Water Requirement ◽  
Growth Stages ◽  
Effective Rainfall ◽  
Crop Water ◽  
Crop Growth Stages ◽  
Reference Crop

This study is about estimation of crop water requirement for rice-wheat and rice-rabi maize cropping system for Pusa Region of Samastipur district of Bihar using CROPWAT model for year 2017-18.The effective rainfall was calculated using USDA S.C. Method. Reference crop evaporation was calculated using meteorological data viz temperature, relative humidity, wind speed and Sunshine using Penman Monteith equation. The meteorological data were collected from university observatory of R.P.C.A.U Pusa. Crop coefficient (Kc) value was taken according to crop growth stages. Effective rainfall and crop water requirement was used for determining net irrigation requirement. The annual crop water requirement of Rice- Wheat cropping system was found to be 904.1 mm whereas the crop-water requirement of Rice- Rabi Maize cropping system was 991.7 mm.

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